Radio channel models laboratory comparative study for the L-band DAB/DMB system
Bibliographic record
Abstract
This paper presents the performance results of multipath fading characterizations performed in the laboratory with a single and dual-antenna DAB/DMB software receiver, using a sophisticated hardware channel simulator (Propsim FE). Different radio channel models corresponding to various scenarios and environments (urban, suburban, indoor, outdoor, SFN) have been tested and compared. For a single antenna scenario, it is shown that for the same environments, the COST207 and 3GPP channel models offer almost a similar performance. Moreover, it is found that in the case of indoor channel reception (WLAN profiles) and for a carrier to noise ratio (C/N) of 15-20 dB, the receiver maintains an acceptable level of pcBER for proper audio decoding. This observation also applies to the recently proposed DVB-H multipath channel profiles, except for the motorway rural channel (MR) at 100 km/h where the receiver fails to operate correctly at this mobile speed. In dual-antenna scenarios, the maximal ratio combining (MRC) diversity technique provides a clear improvement to the performance of the dual-antenna based DAB/DMB receiver for all multipath channel profiles tested. Finally, it is shown that DAB/DMB transmission with a simple delay diversity scheme can be effective to mitigate multipath flat fading degradation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".